


from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        TFDeiTForImageClassification,
        TFDeiTForImageClassificationWithTeacher,
        TFDeiTForMaskedImageModeling,
        TFDeiTModel,
    )
    from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import DeiTImageProcessor

class 							lowercase  :







     def __init__(						self :						List[Any]   ,   _lowercase :						List[str]   ,   _lowercase :						Dict=13   ,   _lowercase :						Any=30   ,   _lowercase :						Optional[int]=2   ,   _lowercase :						List[str]=3   ,   _lowercase :						Optional[Any]=True   ,   _lowercase :						Optional[Any]=True   ,   _lowercase :						Tuple=32   ,   _lowercase :						Any=2   ,   _lowercase :						int=4   ,   _lowercase :						str=37   ,   _lowercase :						str="gelu"   ,   _lowercase :						int=0.1   ,   _lowercase :						Tuple=0.1   ,   _lowercase :						int=10   ,   _lowercase :						Union[str, Any]=0.02   ,   _lowercase :						Any=3   ,   _lowercase :						str=None   ,   _lowercase :						int=2   ,   ):
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		parent
           SCREAMING_SNAKE_CASE__       :  Any						     =		batch_size
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		image_size
           SCREAMING_SNAKE_CASE__       :  str						     =		patch_size
           SCREAMING_SNAKE_CASE__       :  str						     =		num_channels
           SCREAMING_SNAKE_CASE__       :  Dict						     =		is_training
           SCREAMING_SNAKE_CASE__       :  str						     =		use_labels
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		hidden_size
           SCREAMING_SNAKE_CASE__       :  Any						     =		num_hidden_layers
           SCREAMING_SNAKE_CASE__       :  Dict						     =		num_attention_heads
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		intermediate_size
           SCREAMING_SNAKE_CASE__       :  int						     =		hidden_act
           SCREAMING_SNAKE_CASE__       :  str						     =		hidden_dropout_prob
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE__       :  str						     =		type_sequence_label_size
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		initializer_range
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		scope
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		encoder_stride

           # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		(image_size // patch_size) ** 2
           SCREAMING_SNAKE_CASE__       :  Dict						     =		num_patches + 2







     def 							lowercase__       (						self :						Union[str, Any]      ):
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		None
           if self.use_labels:
                 SCREAMING_SNAKE_CASE__       :  Tuple						     =		ids_tensor([self.batch_size]   ,   self.type_sequence_label_size      )

           SCREAMING_SNAKE_CASE__       :  Dict						     =		self.get_config()

           return config, pixel_values, labels







     def 							lowercase__       (						self :						str      ):
           return DeiTConfig(
               image_size=self.image_size   ,   patch_size=self.patch_size   ,   num_channels=self.num_channels   ,   hidden_size=self.hidden_size   ,   num_hidden_layers=self.num_hidden_layers   ,   num_attention_heads=self.num_attention_heads   ,   intermediate_size=self.intermediate_size   ,   hidden_act=self.hidden_act   ,   hidden_dropout_prob=self.hidden_dropout_prob   ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,   is_decoder=_lowercase   ,   initializer_range=self.initializer_range   ,   encoder_stride=self.encoder_stride   ,   )







     def 							lowercase__       (						self :						List[Any]   ,   _lowercase :						Dict   ,   _lowercase :						Any   ,   _lowercase :						str      ):
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		TFDeiTModel(config=_lowercase      )
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		model(_lowercase      )
           self.parent.assertEqual(result.last_hidden_state.shape   ,   (self.batch_size, self.seq_length, self.hidden_size)      )







     def 							lowercase__       (						self :						Optional[int]   ,   _lowercase :						Union[str, Any]   ,   _lowercase :						int   ,   _lowercase :						Tuple      ):
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		TFDeiTForMaskedImageModeling(config=_lowercase      )
           SCREAMING_SNAKE_CASE__       :  Any						     =		model(_lowercase      )
           self.parent.assertEqual(
               result.reconstruction.shape   ,   (self.batch_size, self.num_channels, self.image_size, self.image_size)      )

           # test greyscale images
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		1
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		TFDeiTForMaskedImageModeling(_lowercase      )

           SCREAMING_SNAKE_CASE__       :  Any						     =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		model(_lowercase      )
           self.parent.assertEqual(result.reconstruction.shape   ,   (self.batch_size, 1, self.image_size, self.image_size)      )







     def 							lowercase__       (						self :						List[Any]   ,   _lowercase :						Any   ,   _lowercase :						List[Any]   ,   _lowercase :						List[str]      ):
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		self.type_sequence_label_size
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		TFDeiTForImageClassification(_lowercase      )
           SCREAMING_SNAKE_CASE__       :  Dict						     =		model(_lowercase   ,   labels=_lowercase      )
           self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.type_sequence_label_size)      )

           # test greyscale images
           SCREAMING_SNAKE_CASE__       :  str						     =		1
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		TFDeiTForImageClassification(_lowercase      )

           SCREAMING_SNAKE_CASE__       :  Any						     =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		model(_lowercase   ,   labels=_lowercase      )
           self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.type_sequence_label_size)      )







     def 							lowercase__       (						self :						Optional[int]      ):
           SCREAMING_SNAKE_CASE__       :  str						     =		self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  str						     =		config_and_inputs
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		{'''pixel_values''': pixel_values}
           return config, inputs_dict




@require_tf
class 							lowercase  (						_UpperCAmelCase    ,       _UpperCAmelCase    ,       unittest.TestCase			):
     lowerCamelCase			:			Optional[Any]      = (
         (
             TFDeiTModel,
             TFDeiTForImageClassification,
             TFDeiTForImageClassificationWithTeacher,
             TFDeiTForMaskedImageModeling,
         )
         if is_tf_available()
         else ()
     )
     lowerCamelCase			:			Optional[Any]      = (
         {
             '''feature-extraction''': TFDeiTModel,
             '''image-classification''': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
         }
         if is_tf_available()
         else {}
     )

     lowerCamelCase			:			Optional[Any]      = False
     lowerCamelCase			:			Optional[Any]      = False
     lowerCamelCase			:			str      = False
     lowerCamelCase			:			List[Any]      = False







     def 							lowercase__       (						self :						Optional[Any]      ):
           SCREAMING_SNAKE_CASE__       :  Dict						     =		TFDeiTModelTester(self      )
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		ConfigTester(self   ,   config_class=_lowercase   ,   has_text_modality=_lowercase   ,   hidden_size=37      )







     def 							lowercase__       (						self :						Dict      ):
           self.config_tester.run_common_tests()







     @unittest.skip(reason='''DeiT does not use inputs_embeds'''      )
     def 							lowercase__       (						self :						Any      ):
           pass







     def 							lowercase__       (						self :						List[str]      ):
           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 SCREAMING_SNAKE_CASE__       :  Any						     =		model_class(_lowercase      )
                 self.assertIsInstance(model.get_input_embeddings()   ,   (tf.keras.layers.Layer)      )
                 SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(_lowercase   ,   tf.keras.layers.Dense      )      )







     def 							lowercase__       (						self :						int      ):
           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 SCREAMING_SNAKE_CASE__       :  List[str]						     =		model_class(_lowercase      )
                 SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		inspect.signature(model.call      )
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 SCREAMING_SNAKE_CASE__       :  str						     =		[*signature.parameters.keys()]

                 SCREAMING_SNAKE_CASE__       :  int						     =		['''pixel_values''']
                 self.assertListEqual(arg_names[:1]   ,   _lowercase      )







     def 							lowercase__       (						self :						int      ):
           SCREAMING_SNAKE_CASE__       :  Any						     =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*_lowercase      )







     def 							lowercase__       (						self :						Optional[Any]      ):
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*_lowercase      )







     def 							lowercase__       (						self :						Any      ):
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*_lowercase      )







     def 							lowercase__       (						self :						Tuple   ,   _lowercase :						Dict   ,   _lowercase :						Dict   ,   _lowercase :						List[Any]=False      ):
           SCREAMING_SNAKE_CASE__       :  int						     =		super()._prepare_for_class(_lowercase   ,   _lowercase   ,   return_labels=_lowercase      )

           if return_labels:
                 if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call      ).parameters:
                       del inputs_dict["labels"]

           return inputs_dict







     @slow
     def 							lowercase__       (						self :						str      ):
           for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 SCREAMING_SNAKE_CASE__       :  List[str]						     =		TFDeiTModel.from_pretrained(_lowercase      )
                 self.assertIsNotNone(_lowercase      )




def    a   (							)	-> int:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  str						     =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
      return image





@require_tf
@require_vision
class 							lowercase  (						unittest.TestCase			):







     @cached_property
     def 							lowercase__       (						self :						int      ):
           return (
               DeiTImageProcessor.from_pretrained('''facebook/deit-base-distilled-patch16-224'''      )
               if is_vision_available()
               else None
           )







     @slow
     def 							lowercase__       (						self :						Dict      ):
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		TFDeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224'''      )

           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		self.default_image_processor
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		prepare_img()
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		image_processor(images=_lowercase   ,   return_tensors='''tf'''      )

           # forward pass
           SCREAMING_SNAKE_CASE__       :  str						     =		model(**_lowercase      )

           # verify the logits
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		tf.TensorShape((1, 10_00)      )
           self.assertEqual(outputs.logits.shape   ,   _lowercase      )

           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		tf.constant([-1.0266, 0.1912, -1.2861]      )

           self.assertTrue(np.allclose(outputs.logits[0, :3]   ,   _lowercase   ,   atol=1E-4      )      )











'''simple docstring'''

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def 				a  (     UpperCamelCase_    :    Any			)       ->	List[str]:
		snake_case__	     =os.path.join(args.tf_model_dir      ,					'parameters.json'			)
		snake_case__	     =json.loads(open(UpperCamelCase_			).read()			)
		if not params:
				raise ValueError(
				    f"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""			)
		if not args.output.endswith('.pt'			):
				snake_case__	     =args.output + '.pt'
		snake_case__	     =OrderedDict()
		with tf.device('/CPU:0'			):
				snake_case__	     =tf.train.load_checkpoint(args.tf_model_dir			)
				snake_case__	     =reader.get_variable_to_shape_map()
				for key_name in shapes.keys():
						snake_case__	     =reader.get_tensor(UpperCamelCase_			).astype(np.floataa			)
						if key_name.endswith('/adam_m'			) or key_name.endswith('/adam_v'			):
								continue
						if key_name.startswith('pasts/'			):
								if key_name.startswith('pasts/mlp'			):
										snake_case__	     =int(key_name[9]			)
								elif key_name.startswith('pasts/out'			):
										snake_case__	     =8
								snake_case__	     ='model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
								snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
								snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/moe'			):
								snake_case__	     =int(key_name[9:].split('/'			)[0]			)
								if key_name.endswith('/switch_gating/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/softmlp/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/wo/kernel'			) or key_name.endswith('/wi/kernel'			):
										snake_case__	     =key_name[-9:-7]
										for i in range(16			):
												snake_case__	     ='model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
												snake_case__	     =(
												    vnp[i].transpose([1, 0]			).copy()
												)  # In Mesh-Tensorflow, it is one array, so it is divided
												snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/mlp'			):
								snake_case__	     =int(key_name[9:].split('/'			)[0]			)
								if key_name.endswith('/p1/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wi.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/p1/bias'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wi.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/p2/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wo.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/p2/bias'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wo.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/ln'			):
								snake_case__	     =int(key_name[8:].split('/'			)[0]			)
								if key_name.endswith('/b'			):
										snake_case__	     ='model.blocks.%d.feed_forward.norm.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/g'			):
										snake_case__	     ='model.blocks.%d.feed_forward.norm.weight' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/att'			):
								snake_case__	     =int(key_name[9:].split('/'			)[0]			)
								if key_name.endswith('/qkv/kernel'			):
										snake_case__	     =vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
										snake_case__	     =state[:, 0, :, :]
										snake_case__	     =state[:, 1, :, :]
										snake_case__	     =state[:, 2, :, :]
										snake_case__	     =(
										    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =(
										    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =(
										    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
										snake_case__	     =torch.tensor(UpperCamelCase_			)
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
										snake_case__	     =torch.tensor(UpperCamelCase_			)
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/o/kernel'			):
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
										snake_case__	     =(
										    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]			).transpose([1, 0]			).copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/an'			):
								snake_case__	     =int(key_name[8:].split('/'			)[0]			)
								if key_name.endswith('/b'			):
										snake_case__	     ='model.blocks.%d.self_attn.norm.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/g'			):
										snake_case__	     ='model.blocks.%d.self_attn.norm.weight' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif (
						    key_name.startswith('model/wte'			)
						    or key_name.startswith('model/wpe'			)
						    or key_name.startswith('model/ete'			)
						):
								snake_case__	     ={'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
								    key_name[-3:]
								]
								snake_case__	     ='model.%s.weight' % nlayer
								snake_case__	     =vnp.copy()  # same in embedded
								snake_case__	     =torch.tensor(UpperCamelCase_			)
								if key_name.startswith('model/wte'			):
										snake_case__	     ='lm_head.weight'
										snake_case__	     =vnp.copy()  # same in embedded
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/wob'			):
								snake_case__	     ='final_logits_bias'
								snake_case__	     =vnp.copy()  # same in embedded
								snake_case__	     =state.reshape((1, -1)			)
								snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name == "model/dense/kernel":
								snake_case__	     ='model.last_project.weight'
								snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
								snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name == "model/dense_1/bias":
								snake_case__	     ='model.last_project.bias'
								snake_case__	     =vnp.copy()  # same because it is one dimensional
								snake_case__	     =torch.tensor(UpperCamelCase_			)
		torch.save(UpperCamelCase_      ,					args.output			)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE__     :						int	=  argparse.ArgumentParser(
			    description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
			)
			parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
			parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
			SCREAMING_SNAKE_CASE__     :						str	=  parser.parse_args()
			convert_tf_gptsan_to_pt(args)




